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Probabilistic hypergraph matching based on affinity tensor updating

NEUROCOMPUTING 2017年 269卷 142-147页

作者： Yang, Xu Liu, Zhi-Yong Qiao, Hong Su, Jian-Hua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Graph matching is a fundamental problem in artificial intelligence and structural data processing. Hypergraph matching has recently become popular in the graph matching community. Existing hypergraph matching algorithms usually resort to the continuous methods, while the combinatorial nature of hypergraph matching is not well considered. Therefore in this paper, we propose a novel hypergraph matching algorithm by introducing the affinity tensor updating based graduated projection. Specifically, the hypergraph matching problem is first formulated as a combinatorial optimization problem in a high order polynomial form. Then this NP-hard problem is relaxed and interpreted in a probabilistic manner, which is approximately solved by iterative techniques. The updating of the affinity tensor is performed in each iteration, besides the updating of probabilistic assignment vector. Experimental results on both synthetic and real-world datasets witness the effectiveness of the proposed method. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Hypergraph matching Probabilistic graph matching Tensor decomposition structural pattern recognition

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MINIMIZATION OF structural INFORMATION - A SET-THEORETICAL APPROACH

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pattern recognition 1983年第2期16卷 231-242页

作者： COLLARD, RFA BUFFART, HFJM University of Nijmegen Montessorilaan 3 P.O. Box 9104 6500 HE Nijmegen The Netherlands

According to the coding theory of pattern perception, a pattern may be encoded economically in the form of a hierarchy of operations. In essence, the shortest codes reflect the way(s) a pattern is perceived. Theoretical predictions so far prove consistently correct. The present study proposes a set-theoretical approach in such a way that structural information, defined as classes of patterns having the same structural relationships, induces a redundancy order of codes. In this order, codes may be reduced automatically from an initial sequential encoding. Closure under set-intersection accounts for ambiguity.

关键词： structural pattern recognition Coding theory of pattern perception Descriptional complexity Minimization structural information Redundancy order Automatic code reduction Multiple coding

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ORGANIZATION OF RELATIONAL MODELS FOR SCENE ANALYSIS

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 1982年第6期4卷 595-602页

作者： SHAPIRO, LG HARALICK, RM SENIOR MEMBER IEEE Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA 24061

Relational models are commonly used in scene analysis systems. Most such systems are experimental and deal with only a small number of models. Unknown objects to be analyzed are usually sequentially compared to each model. In this paper, we present some ideas for organizing a large database of relational models. We define a simple relational distance measure, prove it is a metric, and using this measure, describe two organizational/access methods: clustering and binary search trees. We illustrate these methods with a set of randomly generated graphs.","doi":"10.1109/TPAMI.1982.4767312","publicationTitle":"IEEE Transactions on pattern Analysis and Machine Intelligence","startPage":"595","endPage":"602","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&=0162-8828&title=Organization+of+Relational+Models+for+Scene+Analysis&isbn=&publicationDate=Nov.+1982&author=Linda+G.+Shapiro&ContentID=10.1109/TPAMI.1982.4767312&orderBeanReset=true&startPage=595&endPage=602&volumeNum=PAMI-4&issueNum=6","displayPublicationTitle":"IEEE Transactions on pattern Analysis and Machine Intelligence","pdfPath":"/iel5/34/4767305/***","keywords":[{"type":"IEEE Keywords","kwd":["Image analysis","Indexing","Organizing","Relational databases","pattern matching","pattern recognition","Layout","Binary search trees","Image processing","Shape"]},{"type":"Author Keywords ","kwd":["structural pattern recognition","Database organization","graphs","relational distance","relational matching","relational models"]}],"allowComments":false,"pubLink":"/xpl/***?punumber=34","issueLink":"/xpl/***?isnumber=4767305","standardTitle":"Organization of Relational Models for Scene Analysis

关键词： Image analysis Indexing Organizing Relational databases pattern matching pattern recognition Layout Binary search trees Image processing Shape Author Keywords structural pattern recognition Database organization graphs relational distance relational matching relational models

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THE ARTIFICIAL-INTELLIGENCE APPROACH TO pattern-recognition - A PERSPECTIVE AND AN OVERVIEW

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pattern recognition 1985年第6期18卷 383-389页

作者： NANDHAKUMAR, N AGGARWAL, JK Laboratory for Image and Signal Analysis College of Engineering The University of Texas at Austin Austin Texas 78712 U.S.A

The present paper reviews the techniques for automated extraction of information from signals. The techniques may be classified broadly into two categories—the conventional pattern recognition approach and the artificial intelligence (AI) based approach. The conventional approach comprises two methodologies—statistical and structural. The paper reviews salient ❉s in the application of conventional techniques for extraction of information. The systems that use the artificial intelligence approach are characterized with respect to three key properties. The basic differences between the approaches and the computational aspects are reviewed. Current trends in the use of the AI approach are indicated. Some key ideas in current literature are reviewed.

关键词： Artificial intelligence pattern recognition Signal understanding Knowledge based systems Classification Knowledge representation Inference mechanisms Blackboard control structure Computational aspects of pattern recognition Statistical pattern recognition structural pattern recognition Syntactic pattern recognition

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PROBLEM REDUCTION REPRESENTATION FOR THE LINGUISTIC ANALYSIS OF WAVEFORMS

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 1983年第3期5卷 287-298页

作者： STOCKMAN, GC KANAL, LN UNIV MARYLAND DEPT COMP SCICOLLEGE PKMD 20742

This paper shows how the nondirectional structural analysis of pattern data can be performed by matching a problem reduction representation (PRR) of pattern structure with sample data, using a best-first state space search algorithm called SSS*. The end result of the matching algorithm is a tree whose nodes represent recognized structures in the data. Tip nodes of the tree structure correspond to primitives which are recognized in the raw data by curve fitting routines. The operators of the algorithm allow the tree to be constructed with a combination of top-down or bottom-up steps. The matching of the structure tree to waveform segments need not be done in a left-right sequence. Moreover ambiguous matches are pursued in a best first order by using state space search with partial parse trees as states. A software system called WAPSYS (for waveform parsing system) is described, which implements this structural analysis paradigm. Experience using WAPSYS to analyze carotid pulse waves is also discussed.

关键词： pattern recognition Algorithm design and analysis pattern matching Context modeling Data analysis State-space methods Computer science pattern analysis Performance analysis Tree data structures Author Keywords waveform analysis AND/OR graphs medical waveforms structural pattern recognition syntactic pattern recognition

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Fuzzy spatial relationships for image processing and interpretation: a review

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IMAGE AND VISION COMPUTING 2005年第2期23卷 89-110页

作者： Bloch, I LTCI CNRS UMR 5141 Ecole Natl Super TelecommunDept TSI F-75013 Paris France

In spatial reasoning, relationships between spatial entities play a major role. In image interpretation, computer vision and structural recognition, the management of imperfect information and of imprecision constitutes a key point. This calls for the framework of fuzzy sets, which exhibits nice features to represent spatial imprecision at different levels, imprecision in knowledge and knowledge representation, and which provides powerful tools for fusion, decision-making and reasoning. In this paper, we review the main fuzzy approaches for defining spatial relationships including topological (set relationships, adjacency) and metrical relations (distances, directional relative position). (C) 2004 Elsevier B.V. All rights reserved.

关键词： fuzzy spatial relationships degree of intersection degree of inclusion degree of adjacency distances directional relative position structural pattern recognition image interpretation spatial reasoning

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structural matching of 2D electrophoresis gels using deformed graphs

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pattern recognition LETTERS 2011年第1期32卷 3-11页

作者： Noma, Alexandre Pardo, Alvaro Cesar, Roberto M., Jr. Univ Sao Paulo Inst Math & Stat BR-05508090 Sao Paulo Brazil Catholic Univ Uruguay Dept Elect Engn Fac Engn & Technol Montevideo 11600 Uruguay

2D electrophoresis is a well-known method for protein separation which is extremely useful in the field of proteomics. Each spot in the image represents a protein accumulation and the goal is to perform a differential analysis between pairs of images to study changes in protein content. It is thus necessary to register two images by finding spot correspondences. Although it may seem a simple task, generally, the manual processing of this kind of images is very cumbersome, especially when strong variations between corresponding sets of spots are expected (e.g. strong non-linear deformations and outliers). In order to solve this problem, this paper proposes a new quadratic assignment formulation together with a correspondence estimation algorithm based on graph matching which takes into account the structural information between the detected spots. Each image is represented by a graph and the task is to find a maximum common subgraph. Successful experimental results using real data are presented, including an extensive comparative performance evaluation with ground-truth data. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Deformed graphs Electrophoresis gels Graph matching Maximum common subgraph Point matching structural pattern recognition

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Graph embedding in vector spaces by node attribute statistics

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pattern recognition 2012年第9期45卷 3072-3083页

作者： Gibert, Jaume Valveny, Ernest Bunke, Horst Univ Autonoma Barcelona Comp Vis Ctr Bellaterra 08193 Spain Univ Bern Inst Comp Sci & Appl Math CH-3012 Bern Switzerland

Graph-based representations are of broad use and applicability in pattern recognition. They exhibit, however, a major drawback with regards to the processing tools that are available in their domain. Graph embedding into vector spaces is a growing field among the structural pattern recognition community which aims at providing a feature vector representation for every graph, and thus enables classical statistical learning machinery to be used on graph-based input patterns. In this work, we propose a novel embedding methodology for graphs with continuous node attributes and unattributed edges. The approach presented in this paper is based on statistics of the node labels and the edges between them, based on their similarity to a set of representatives. We specifically deal with an important issue of this methodology, namely, the selection of a suitable set of representatives. In an experimental evaluation, we empirically show the advantages of this novel approach in the context of different classification problems using several databases of graphs. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： structural pattern recognition Graph embedding Data clustering Graph classification

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Neuronal Groups and Interrelations

Neuronal Groups and Interrelations

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International Multiconference on Computer Science and Information Technology

作者： Pryczek, Michal Tech Univ Lodz Inst Comp Sci PL-90924 Lodz Poland

ISBN: (纸本)978836810149

Recent development of various domains of Artificial Intelligence including Information Retrieval and Text/Image Understanding created demand on new, sophisticated, contextual methods for data analysis. This article formulates Neuronal Group and Extended Neuron Somatic concepts that can be vastly used in creating such methods. Neural interrelations are described using graphs, construction of which is done in parallel with neural network learning. Prototype technique based on Growing Neural Gas is also presented to give more detailed view.

关键词： neural networks pattern recognition structural pattern recognition pattern classification

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On Information Granulation via Data Filtering for Granular Computing-Based pattern recognition: A Graph Embedding Case Study

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SN Computer Science 2023年第3期4卷 314页

作者： Martino, Alessio De Santis, Enrico Rizzi, Antonello Department of Business and Management LUISS University Viale Romania 32 Rome 00197 Italy Department of Information Engineering Electronics and Telecommunications University of Rome “La Sapienza” Via Eudossiana 18 Rome 00184 Italy

Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this companion paper, we follow our previous paper (Martino et al. in Algorithms 15(5):148, 2022) in the context of comparing different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process and, conversely, to our previous work, we employ a filtering-based approach for the synthesis of information granules instead of a clustering-based one. Computational results on 6 open-access data sets corroborate the robustness of our filtering-based approach with respect to data stratification, if compared to a clustering-based granulation stage. © 2023, The Author(s).

关键词： Data mining and knowledge discovery Granular computing Graph classification Inexact graph matching Information granulation structural pattern recognition Supervised learning

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